Please use this identifier to cite or link to this item: https://ah.lib.nccu.edu.tw/handle/140.119/73673
DC FieldValueLanguage
dc.contributor資管系
dc.creator苑守慈zh_TW
dc.creatorShao, Yuehjen E.;Lin, Yue-Fa;Yuan, Soe-Tsyr
dc.date1999
dc.date.accessioned2015-03-05T06:03:25Z-
dc.date.available2015-03-05T06:03:25Z-
dc.date.issued2015-03-05T06:03:25Z-
dc.identifier.urihttp://nccur.lib.nccu.edu.tw/handle/140.119/73673-
dc.description.abstractThis study examines the data that result from multiple promotional strategies when the data are autocorrelated. Time series intervention analysis is the traditional way to analyze such data, focusing on the effects of a single or a few interventions. Time series intervention analysis delivers good results, provided that there is a known and predetermined schedule of future interventions. This study opts for a different type of analysis. Instead of adopting the traditional time series intervention analysis with only one or a few interventions, this study explores the possibility of integrating time series intervention analysis and a knowledge-based system to analyze multiple-interventions data. This integrated approach does not require attempts to ascertain the effects of future interventions. Through the analysis of actual promotion data, this study shows the benefits of using the proposed method.
dc.format.extent210220 bytes-
dc.format.mimetypeapplication/pdf-
dc.relationInternational Journal of Applied Statistics,26(6),755-766
dc.titleIntegrated application of time series multiple-interventions analysis and knowledge-based reasoning
dc.typearticleen
dc.identifier.doi10.1080/02664769922197en_US
dc.doi.urihttp://dx.doi.org/10.1080/02664769922197 en_US
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypearticle-
item.fulltextWith Fulltext-
item.grantfulltextrestricted-
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